Lung cancer is the leading cause of cancer mortality in the United States [1], and non small cell lung cancer (NSCLC) is the predominant subtype of this malignancy. Mutations in the KRAS oncogene characterize 30- 40% of all NSCLC malignancies, and a sub-set of those tumors depend on mutant KRAS for survival. However, the specific network of effector genes and proteins and the phosphorylation cascade that drive carcinogenesis in KRAS dependent NSCLC remains poorly understood. In addition, the development of inhibitors of Ras oncoprotein and drug therapies aimed at blocking Ras pathways has proven unsuccessful [2]. Thus far, studies aimed at characterizing KRAS signaling pathway have applied conventional molecular and biochemical methodologies or high throughput transcriptomic and proteomic technologies in isolation. None have attempted to synthesize information from multiple profiling platforms. Therefore, development of robust unbiased methodologies for synthesizing global transcriptomic, proteomic and phosphoproteomic data is warranted in order to gain a comprehensive understanding of the mechanism of how mutant KRAS promotes cancer development and progression. Here, we present preliminary data obtained by the bioinformatic integration of global gene expression and phospho-proteome profiling in a panel of RAS NSCLC mutant cell lines. We integrated for the first time, next generation RNA sequencing (RNA-seq) data and tyrosine phosphorylation by LC-MS/MS data to obtain a transcript expression and a phosphoproteome signature that not only recapitulated previous knowledge about RAS pathway, but also suggest unknown aspects of the Ras pathway. In this application, two aims will extend this approach with the goal of reconstructing the active KRAS network in NSCLC. Specific Aim 1: Characterize transcriptome and phospho-proteome signatures associated with KRAS oncogene dependency in NSCLC cell lines. Specific Aim 2: Reconstruct active molecular networks associated with KRAS dependency by synthesizing transcriptome and phosphoproteome signatures, and apply them to classify NSCLC tumor samples. Finally, we expect that the results of this project will shed light on selecting therapeutic drug targets to treat cancers driven by the KRAS oncogene.